The broad goals of this project are to extend and deepen our understanding of the properties of neural circuits. The vertebrate retina is chosen as a model system, because of its ease of experimental access and its complex anatomy. When neurons are hooked together into a circuit, two properties become important: first, the activity of the neurons means something in the context of the animal; second, neurons perform a computation on their inputs. We thus will study how the retina encodes and processes visual information. The specific aims are: 1) perform a functional classification of retinal ganglion cells using information theoretic techniques, 2) acquire a database of natural movie clips and categorize their statistics, 3) build a spike word dictionary to efficiently capture nonlinear processing in the retina, and 4) investigate the generality of retinal adaptation and characterize its effects on the neural code. A detailed knowledge of what image processing occurs in the retina is of fundamental interest to neuroscience and is also important for a retinal prosthesis to be able to restore vision successfully.